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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.29

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the bigbio/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.
        Report generated on 2025-07-01, 14:33 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant.URL: https://github.com/bigbio/pmultiqc


        Experiment Setup

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.
        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html
        Showing 11/11 rows and 8/8 columns.
        Sample NameMSstats Condition: SPMSstats Condition: CTMSstats Condition: QYMSstats Condition: CVMSstats BioReplicateFraction GroupFractionLabel
         
        1
        Saccharomyces cerevisiaeMixture10%in-house1
         
         ↳ a05191
        111
         
        2
        Saccharomyces cerevisiaeMixture10%in-house2
         
         ↳ a05192
        211
         
        3
        Saccharomyces cerevisiaeMixture10%in-house3
         
         ↳ a05194
        311
         
        4
        Saccharomyces cerevisiaeMixture5%in-house4
         
         ↳ a05195
        411
         
        5
        Saccharomyces cerevisiaeMixture5%in-house5
         
         ↳ a05196
        511
         
        6
        Saccharomyces cerevisiaeMixture5%in-house6
         
         ↳ a05197
        611
         
        7
        Saccharomyces cerevisiaeMixture5%in-house7
         
         ↳ a05198
        711
         
        8
        Saccharomyces cerevisiaeMixture3.3%in-house8
         
         ↳ a05199
        811
         
        9
        Saccharomyces cerevisiaeMixture3.3%in-house9
         
         ↳ a05200
        911
         
        10
        Saccharomyces cerevisiaeMixture3.3%in-house10
         
         ↳ a05201
        1011
         
        11
        Saccharomyces cerevisiaeMixture3.3%in-house11
         
         ↳ a05202
        1111

        Summary and HeatMap

        Summary Table

        This table shows the quantms pipeline summary statistics.
        This table shows the quantms pipeline summary statistics.
        Showing 1/1 rows and 5/5 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified#Proteins Quantified
        806748
        537848
        66.67%
        61110
        9346
        8458

        HeatMap

        This heatmap provides an overview of the performance of the quantms.
        This plot shows the pipeline performance overview. Some metrics are calculated. * Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants * Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25. * Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544). * Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file * Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study. * Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad. * Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. * Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
        Created with MultiQC

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result.
        Including Sample Name, Possible Study Variables, identified the number of peptide in the pipeline, and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the `remove_decoy` parameter.
        Showing 11/11 rows and 9/9 columns.
        Sample NameMSstats Condition: SPMSstats Condition: CTMSstats Condition: QYMSstats Condition: CVFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
         
        1
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05191
        1
        35347
        32810
        4730
        7417
         
        2
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05192
        1
        37165
        34620
        2533
        7859
         
        3
        Saccharomyces cerevisiaeMixture10%in-house
         
         ↳ a05194
        1
        37815
        35263
        1912
        7959
         
        4
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05195
        1
        37130
        34616
        2013
        7757
         
        5
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05196
        1
        37172
        34700
        1772
        7710
         
        6
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05197
        1
        37289
        34787
        1781
        7696
         
        7
        Saccharomyces cerevisiaeMixture5%in-house
         
         ↳ a05198
        1
        37053
        34559
        1740
        7726
         
        8
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05199
        1
        36879
        34406
        1946
        7611
         
        9
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05200
        1
        37300
        34850
        1927
        7632
         
        10
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05201
        1
        37239
        34751
        1735
        7633
         
        11
        Saccharomyces cerevisiaeMixture3.3%in-house
         
         ↳ a05202
        1
        36919
        34486
        1697
        7637

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in quantms pipeline final result
        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        ProteinGroups Count

        Number of protein groups per raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Peptide ID Count

        Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Missed Cleavages Per Raw File

        Missed Cleavages per raw file.
        Under optimal digestion conditions (high enzyme grade etc.), only few missed cleavages (MC) are expected. In general, increased MC counts also increase the number of peptide signals, thus cluttering the available space and potentially provoking overlapping peptide signals, biasing peptide quantification. Thus, low MC counts should be favored. Interestingly, it has been shown recently that incorporation of peptides with missed cleavages does not negatively influence protein quantification (see [Chiva, C., Ortega, M., and Sabido, E. Influence of the Digestion Technique, Protease, and Missed Cleavage Peptides in Protein Quantitation. J. Proteome Res. 2014, 13, 3979-86](https://doi.org/10.1021/pr500294d) ). However this is true only if all samples show the same degree of digestion. High missed cleavage values can indicate for example, either a) failed digestion, b) a high (post-digestion) protein contamination, or c) a sample with high amounts of unspecifically degraded peptides which are not digested by trypsin. If MC>=1 is high (>20%) you should increase the missed cleavages settings in MaxQuant and compare the number of peptides. Usually high MC correlates with bad identification rates, since many spectra cannot be matched to the forward database. In the rare case that 'no enzyme' was specified in MaxQuant, neither scores nor plots are shown.
        Created with MultiQC

        Modifications Per Raw File

        Compute an occurence table of modifications (e.g. Oxidation (M)) for all peptides, including the unmodified.
        Post-translational modifications contained within the identified peptide sequence.
        Created with MultiQC

        MS/MS Identified Per Raw File

        MS/MS identification rate per Raw file.
        MS/MS identification rate per raw file (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)
        Created with MultiQC

        Search Engine Scores

        Summary of Spectral E-values

        This statistic is extracted from idXML files. SpecEvalue: Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).
        Created with MultiQC

        Summary of cross-correlation scores

        This statistic is extracted from idXML files. xcorr: cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.
        Created with MultiQC

        Summary of Search Engine PEP

        This statistic is extracted from idXML files.
        Created with MultiQC

        Consensus Across Search Engines

        Consensus support is a measure of agreement between search engines. Every peptide sequence in the analysis has been identified by at least one search run. The consensus support defines which fraction (between 0 and 1) of the remaining search runs "supported" a peptide identification that was kept. The meaning of "support" differs slightly between algorithms: For best, worst, average and rank, each search run supports peptides that it has also identified among its top considered_hits candidates. So the "consensus support" simply gives the fraction of additional search engines that have identified a peptide. (For example, if there are three search runs, peptides identified by two of them will have a "support" of 0.5.) For the similarity-based algorithms PEPMatrix and PEPIons, the "support" for a peptide is the average similarity of the most-similar peptide from each (other) search run.
        Created with MultiQC

        Quantification Analysis

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).
        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms. * BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets. * Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored. * Peptide intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.
        Showing 50/50 rows and 7/7 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage IntensitySP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
        1
        sp|P55011|S12A2_HUMAN
        AAAAAAAAAAAAAAAGAGAGAK
        1.0000
        7.5635
        7.8547
        7.1305
        7.2778
        2
        sp|Q5TF21|SOGA3_HUMAN
        AAAAAAAAQMHAK
        0.9999
        6.8194
        6.8194
        0.0000
        0.0000
        3
        sp|Q9Y4H2|IRS2_HUMAN
        AAAAAAAAVPSAGPAGPAPTSAAGR
        1.0000
        7.5487
        7.5525
        0.0000
        7.5409
        4
        sp|P36578|RL4_HUMAN
        AAAAAAALQAK
        1.0000
        9.0107
        9.0444
        8.8642
        9.0992
        5
        sp|Q6SPF0|SAMD1_HUMAN
        AAAAAATAPPSPGPAQPGPR
        1.0000
        7.5572
        7.4772
        7.5710
        7.6038
        6
        sp|Q8WUQ7|CATIN_HUMAN
        AAAAALSQQQSLQER
        1.0000
        7.2535
        0.0000
        0.0000
        7.2535
        7
        sp|A6NIH7|U119B_HUMAN
        AAAAASAAGPGGLVAGK
        0.9999
        7.6843
        7.6746
        7.6609
        7.7244
        8
        sp|Q9P258|RCC2_HUMAN
        AAAAAWEEPSSGNGTAR
        1.0000
        8.0796
        7.9541
        8.1012
        8.1353
        9
        sp|Q96L91|EP400_HUMAN
        AAAAPFQTSQASASAPR
        1.0000
        7.1562
        7.2321
        7.1622
        7.1302
        10
        sp|P52701|MSH6_HUMAN
        AAAAPGASPSPGGDAAWSEAGPGPR
        1.0000
        7.2481
        7.2991
        7.2426
        7.1980
        11
        sp|P52701|MSH6_HUMAN
        AAAAPGASPSPGGDAAWSEAGPGPRPLAR
        1.0000
        7.5203
        7.4933
        7.5347
        7.5254
        12
        sp|P55036|PSMD4_HUMAN
        AAAASAAEAGIATTGTEDSDDALLK
        1.0000
        7.9610
        7.8871
        7.9954
        7.9677
        13
        sp|A1X283|SPD2B_HUMAN
        AAAASVPNADGLK
        1.0000
        8.0871
        0.0000
        0.0000
        8.0871
        14
        sp|Q8WWH5|TRUB1_HUMAN
        AAAAVVAAAAR
        1.0000
        7.4872
        7.5504
        7.3935
        7.5348
        15
        sp|P09938|RIR2_YEAST
        AAADALSDLEIK
        1.0000
        7.8338
        8.0377
        7.7404
        7.5226
        16
        sp|Q96GQ5|RUSF1_HUMAN
        AAADGSLQWEVGGWR
        0.9977
        7.3983
        7.3983
        0.0000
        0.0000
        17
        sp|O95159|ZFPL1_HUMAN
        AAADSDPNLDPLMNPHIR
        1.0000
        7.4766
        7.4766
        0.0000
        0.0000
        18
        sp|Q6P2E9|EDC4_HUMAN
        AAADTLQGPMQAAYR
        1.0000
        8.2114
        8.1032
        0.0000
        8.2710
        19
        sp|Q9NQP4|PFD4_HUMAN
        AAAEDVNVTFEDQQK
        1.0000
        8.4535
        8.4058
        8.4546
        8.4851
        20
        sp|O15357|SHIP2_HUMAN
        AAAEELLAR
        0.9999
        7.5581
        0.0000
        7.6075
        7.5024
        21
        sp|P30260|CDC27_HUMAN
        AAAEGLMSLLR
        1.0000
        7.2680
        6.9462
        7.3082
        7.3230
        22
        sp|P15180|SYKC_YEAST
        AAAEGVANLHLDEATGEMVSK
        1.0000
        7.8182
        7.9145
        7.7394
        7.5220
        23
        sp|Q9NP50|SHCAF_HUMAN
        AAAEKPEEQGPEPLPISTQEW
        1.0000
        7.0767
        7.2119
        0.0000
        6.8793
        24
        sp|Q01780|EXOSX_HUMAN
        AAAEQAISVR
        1.0000
        7.8330
        0.0000
        0.0000
        7.8330
        25
        sp|P02786|TFR1_HUMAN
        AAAEVAGQFVIK
        1.0000
        7.8392
        7.8840
        7.8143
        7.7924
        26
        sp|P07954|FUMH_HUMAN
        AAAEVNQDYGLDPK
        1.0000
        8.6800
        8.6560
        8.6742
        8.7027
        27
        sp|Q9Y490|TLN1_HUMAN
        AAAFEEQENETVVVK
        1.0000
        8.2954
        8.2969
        8.3068
        8.2885
        28
        sp|O94826|TOM70_HUMAN
        AAAFEQLQK
        1.0000
        8.4147
        8.3728
        8.4191
        8.4394
        29
        sp|Q96S52|PIGS_HUMAN
        AAAGAAATHLEVAR
        1.0000
        8.1765
        8.1765
        0.0000
        0.0000
        30
        sp|Q9H3U1|UN45A_HUMAN
        AAAGGLAMLTSMR
        1.0000
        7.4325
        0.0000
        7.4304
        7.4346
        31
        sp|Q9GZT9|EGLN1_HUMAN
        AAAGGQGSAVAAEAEPGK
        1.0000
        7.5986
        7.5986
        0.0000
        0.0000
        32
        sp|Q9GZT9|EGLN1_HUMAN
        AAAGGQGSAVAAEAEPGKEEPPAR
        1.0000
        7.3369
        7.3028
        7.2321
        7.5142
        33
        sp|P17544|ATF7_HUMAN
        AAAGPLDMSLPSTPDIK
        1.0000
        7.1283
        6.8848
        7.1499
        7.1940
        34
        sp|O14497|ARI1A_HUMAN
        AAAGQESEGPAVGPPQPLGK
        1.0000
        7.8403
        7.8201
        7.8202
        7.8712
        35
        sp|Q9BR61|ACBD6_HUMAN
        AAAHLQGLIQVASR
        1.0000
        7.3271
        7.2245
        7.3748
        7.3664
        36
        sp|P0DMV8|HS71A_HUMAN;sp|P0DMV9|HS71B_HUMAN
        AAAIGIDLGTTYSCVGVFQHGK
        1.0000
        7.1289
        7.1289
        0.0000
        0.0000
        37
        sp|Q8TAE8|G45IP_HUMAN
        AAALAAAVAQDPAASGAPSS
        1.0000
        7.6476
        7.6741
        0.0000
        7.6290
        38
        sp|Q14008|CKAP5_HUMAN
        AAALATVNAWAEQTGMK
        1.0000
        7.6494
        7.5710
        7.6614
        7.6891
        39
        sp|P31948|STIP1_HUMAN
        AAALEAMK
        1.0000
        8.3820
        8.3476
        8.4448
        8.3372
        40
        sp|P31948|STIP1_HUMAN
        AAALEFLNR
        1.0000
        9.0951
        9.0992
        9.1237
        9.0613
        41
        sp|Q68DK7|MSL1_HUMAN
        AAALGGPEDEPGAAEAHFLPR
        1.0000
        7.4992
        0.0000
        7.4992
        0.0000
        42
        sp|Q12955|ANK3_HUMAN
        AAALLLQNDNNADVESK
        1.0000
        6.7923
        6.7923
        0.0000
        0.0000
        43
        sp|Q8NAF0|ZN579_HUMAN
        AAALQALQAQAPTSPPPPPPPLK
        0.9996
        7.0332
        0.0000
        0.0000
        7.0332
        44
        sp|P56945|BCAR1_HUMAN
        AAALQYPSPSAAQDMVER
        1.0000
        7.4761
        7.4345
        0.0000
        7.4956
        45
        sp|Q14498|RBM39_HUMAN
        AAAMANNLQK
        1.0000
        8.0971
        8.0314
        8.1297
        8.1088
        46
        sp|Q9BQ04|RBM4B_HUMAN
        AAAMLPTVGEGYGYGPESELSQASAATR
        1.0000
        6.8052
        6.8052
        0.0000
        0.0000
        47
        sp|Q9BQ04|RBM4B_HUMAN
        AAAMLPTVGEGYGYGPESELSQASAATR
        1.0000
        7.6162
        7.4976
        7.7093
        0.0000
        48
        sp|Q15042|RB3GP_HUMAN
        AAAMTPPEEELK
        1.0000
        8.1021
        0.0000
        0.0000
        8.1021
        49
        sp|Q6UB99|ANR11_HUMAN
        AAAPAEGPPGGIQPEAAEPKPTAEAPK
        1.0000
        7.1285
        0.0000
        7.0511
        7.2512
        50
        sp|P31350|RIR2_HUMAN
        AAAPGVEDEPLLR
        1.0000
        8.4047
        8.4146
        8.4046
        8.3971

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).
        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored. * Protein intensity in each condition (Eg. `CT=Mixture;CN=UPS1;QY=0.1fmol`): Summarize intensity of peptides.
        Showing 50/50 rows and 6/6 columns.
        ProteinIDProtein NameNumber of PeptidesAverage IntensitySP=Saccharomyces cerevisiae;CT=Mixture;QY=10%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=5%;CV=in-houseSP=Saccharomyces cerevisiae;CT=Mixture;QY=3.3%;CV=in-house
        1
        sp|A0A024RBG1|NUD4B_HUMAN;sp|Q9NZJ9|NUDT4_HUMAN
        1
        7.6182
        7.7904
        7.6751
        7.4865
        2
        sp|A0A0A6YYC5|TVA14_HUMAN
        1
        7.8080
        0.0000
        7.8080
        0.0000
        3
        sp|A0A0B4J2D5|GAL3B_HUMAN;sp|P0DPI2|GAL3A_HUMAN
        7
        9.0060
        8.9366
        8.9477
        9.0210
        4
        sp|A0AVF1|IFT56_HUMAN
        3
        7.6152
        7.4290
        7.3572
        7.4632
        5
        sp|A0AVT1|UBA6_HUMAN
        19
        9.1927
        9.0894
        9.1329
        9.2011
        6
        sp|A0FGR8|ESYT2_HUMAN
        10
        8.6129
        8.6282
        8.6154
        8.4218
        7
        sp|A0JLT2|MED19_HUMAN
        1
        7.0329
        7.1629
        7.0350
        6.9910
        8
        sp|A0JNW5|UH1BL_HUMAN
        4
        7.6573
        7.0527
        6.9881
        7.6277
        9
        sp|A0MZ66|SHOT1_HUMAN
        11
        8.5320
        8.1621
        8.5007
        8.3048
        10
        sp|A0PJW6|TM223_HUMAN
        1
        7.2444
        7.1459
        7.2162
        7.3152
        11
        sp|A0PK00|T120B_HUMAN
        2
        7.3972
        0.3010
        7.1447
        7.4736
        12
        sp|A1L020|MEX3A_HUMAN
        8
        8.5322
        8.4172
        8.4686
        8.3708
        13
        sp|A1L0T0|HACL2_HUMAN
        10
        8.4628
        8.4028
        8.2699
        8.3568
        14
        sp|A1L390|PKHG3_HUMAN
        3
        7.4518
        7.0501
        6.9547
        7.3954
        15
        sp|A1X283|SPD2B_HUMAN
        24
        9.1792
        9.0516
        9.1259
        9.1847
        16
        sp|A2RRP1|NBAS_HUMAN
        19
        8.6161
        8.5332
        8.4991
        8.5837
        17
        sp|A2RUC4|TYW5_HUMAN
        4
        7.5948
        7.3995
        6.9391
        7.3579
        18
        sp|A3KMH1|VWA8_HUMAN
        24
        8.7303
        8.6333
        8.4186
        8.5408
        19
        sp|A3KN83|SBNO1_HUMAN
        9
        8.2706
        8.1618
        7.9632
        7.8503
        20
        sp|A4D161|F221A_HUMAN
        2
        8.3032
        7.0180
        8.3041
        7.0936
        21
        sp|A4D1E9|GTPBA_HUMAN
        5
        7.8848
        7.2447
        7.6046
        7.7357
        22
        sp|A4D1P6|WDR91_HUMAN
        4
        8.2410
        6.9871
        7.3815
        8.1742
        23
        sp|A4D2B8|PM2P1_HUMAN
        1
        7.9174
        0.0000
        0.0000
        7.9174
        24
        sp|A5D8V6|VP37C_HUMAN
        1
        7.6780
        7.6054
        7.7104
        7.7100
        25
        sp|A5YKK6|CNOT1_HUMAN
        41
        9.2554
        9.1394
        9.0876
        9.2288
        26
        sp|A5YM72|CRNS1_HUMAN
        1
        7.5357
        7.5357
        0.0000
        0.0000
        27
        sp|A5Z2X5|YP010_YEAST
        1
        6.8123
        6.8123
        0.0000
        0.0000
        28
        sp|A6NCE7|MP3B2_HUMAN;sp|Q9GZQ8|MLP3B_HUMAN
        1
        8.4373
        8.4216
        8.4021
        8.4650
        29
        sp|A6NDB9|PALM3_HUMAN
        4
        7.6389
        7.0067
        7.4330
        7.5512
        30
        sp|A6NDG6|PGP_HUMAN
        4
        8.3821
        8.3570
        8.3769
        8.4088
        31
        sp|A6NDU8|CE051_HUMAN
        2
        7.8688
        7.7968
        7.7529
        7.8533
        32
        sp|A6NFI3|ZN316_HUMAN
        3
        8.2524
        7.5023
        7.4077
        8.0845
        33
        sp|A6NGN9|IGLO5_HUMAN
        5
        8.1099
        7.8933
        8.0293
        8.1413
        34
        sp|A6NHL2|TBAL3_HUMAN
        2
        8.1946
        8.0283
        8.1988
        8.1949
        35
        sp|A6NHQ2|FBLL1_HUMAN
        5
        8.9518
        8.9035
        8.9699
        8.9462
        36
        sp|A6NHR9|SMHD1_HUMAN
        46
        9.7252
        9.5768
        9.5881
        9.7305
        37
        sp|A6NIH7|U119B_HUMAN
        2
        7.7692
        7.6746
        7.7509
        7.8008
        38
        sp|A6NJ78|MET15_HUMAN
        3
        7.7809
        7.2598
        0.4771
        7.8244
        39
        sp|A6NKD9|CC85C_HUMAN
        4
        7.7193
        7.3300
        6.9448
        7.6628
        40
        sp|A6NKG5|RTL1_HUMAN
        41
        10.0303
        9.9912
        10.0242
        10.0413
        41
        sp|A6NNE9|MARHB_HUMAN
        1
        6.7344
        0.0000
        0.0000
        6.7344
        42
        sp|A6ZKI3|RTL8C_HUMAN
        2
        7.1764
        0.3010
        6.7994
        7.1581
        43
        sp|A7E2V4|ZSWM8_HUMAN
        7
        8.3233
        7.4292
        7.7053
        8.3208
        44
        sp|A7KAX9|RHG32_HUMAN
        2
        7.8542
        0.3010
        0.3010
        7.8542
        45
        sp|A8CG34|P121C_HUMAN
        2
        7.7171
        7.6553
        7.6905
        7.7313
        46
        sp|A8MT69|CENPX_HUMAN
        1
        7.7040
        7.6315
        7.7263
        7.6915
        47
        sp|A8MTJ3|GNAT3_HUMAN
        1
        7.3227
        0.0000
        7.3227
        0.0000
        48
        sp|A8MUU1|FB5L3_HUMAN
        1
        8.3119
        8.3119
        0.0000
        0.0000
        49
        sp|A8MVJ9|HPF1L_HUMAN
        1
        7.2282
        0.0000
        0.0000
        7.2282
        50
        sp|A8MVW0|F1712_HUMAN
        3
        7.6607
        7.5248
        7.1330
        7.6752

        Peptide Intensity Distribution

        Peptide intensity per file from mzTab.
        Calculate the average of peptide_abundance_study_variable[1-n] values for each peptide from the peptide table in the mzTab file, and then apply a log2 transformation.
        Created with MultiQC

        MS1 Analysis

        Total Ion Chromatograms

        MS1 quality control information extracted from the spectrum files.
        This plot displays Total Ion Chromatograms (TICs) derived from MS1 scans across all analyzed samples. The x-axis represents retention time, and the y-axis shows the total ion intensity at each time point. Each colored trace corresponds to a different sample. The TIC provides a global view of the ion signal throughout the LC-MS/MS run, reflecting when compounds elute from the chromatography column. Key aspects to assess include: * Overall intensity pattern: A consistent baseline and similar peak profiles across samples indicate good reproducibility. * Major peak alignment: Prominent peaks appearing at similar retention times suggest stable chromatographic performance. * Signal-to-noise ratio: High peaks relative to baseline noise reflect better sensitivity. * Chromatographic resolution: Sharp, well-separated peaks indicate effective separation. * Signal drift: A gradual decline in signal intensity across the run may point to source contamination or chromatography issues. Deviations such as shifted retention times, missing peaks, or inconsistent intensities may signal problems in sample preparation, LC conditions, or mass spectrometer performance that require further investigation.
        Created with MultiQC

        MS1 Base Peak Chromatograms

        MS1 base peak chromatograms extracted from the spectrum files.
        The Base Peak Chromatogram (BPC) displays the intensity of the most abundant ion at each retention time point across your LC-MS run. Unlike the Total Ion Chromatogram (TIC) which shows the summed intensity of all ions, the BPC highlights the strongest signals, providing better visualization of compounds with high abundance while reducing baseline noise. This makes it particularly useful for identifying major components in complex samples, monitoring dominant species, and providing clearer peak visualization when signal-to-noise ratio is a concern. Comparing BPC patterns across samples allows you to evaluate consistency in the detection of high-abundance compounds and can reveal significant variations in sample composition or instrument performance.
        Created with MultiQC

        MS1 Peaks

        MS1 Peaks from the spectrum files
        This plot shows the number of peaks detected in MS1 scans over the course of each sample run. The x-axis represents retention time (in minutes), while the y-axis displays the number of distinct ion signals (peaks) identified in each MS1 scan. The MS1 peak count reflects spectral complexity and provides insight into instrument performance during the LC-MS analysis. Key aspects to consider include: * Overall pattern: Peak counts typically increase during the elution of complex mixtures and decrease during column washing or re-equilibration phases. * Peak density: Higher counts suggest more complex spectra, potentially indicating a greater number of compounds present at that time point." * Peak Consistency across samples: Similar profiles among replicates or related samples indicate good analytical reproducibility. * Sudden drops: Abrupt decreases in peak count may point to transient ionization issues, spray instability, or chromatographic disruptions. * Baseline values: The minimum peak count observed reflects the level of background noise or instrument sensitivity in the absence of eluting compounds. Monitoring MS1 peak counts complements total ion chromatogram (TIC) and base peak chromatogram (BPC) data, offering an additional layer of quality control related to signal complexity, instrument stability, and sample composition.
        Created with MultiQC

        General stats for MS1 information

        General stats for MS1 information extracted from the spectrum files.
        This table presents general statistics for MS1 information extracted from mass spectrometry data files." It displays MS runs with their acquisition dates and times. For each file, the table shows two key metrics: TotalCurrent (the sum of all MS1 ion intensities throughout the run) and ScanCurrent (the sum of MS2 ion intensities). These values provide a quick overview of the total ion signals detected during both survey scans (MS1) and fragmentation scans (MS2), allowing for comparison of overall signal intensity across samples. Consistent TotalCurrent and ScanCurrent values across similar samples typically indicate good reproducibility in the mass spectrometry analysis, while significant variations may suggest issues with sample preparation, instrument performance, or ionization efficiency. The blue shading helps visualize the relative intensity differences between samples.
        Showing 11/11 rows and 3/3 columns.
        FileAcquisition Date Timelog10(Total Current)log10(Scan Current)
        a05191
        2017-04-19 12:31:30
        13.2781
        11.3916
        a05192
        2017-04-19 16:00:49
        13.3983
        11.4832
        a05194
        2017-04-19 23:00:02
        13.4088
        11.5156
        a05195
        2017-04-20 02:29:46
        13.3459
        11.4725
        a05196
        2017-04-20 05:59:32
        13.3672
        11.4841
        a05197
        2017-04-20 09:29:26
        13.3514
        11.4879
        a05198
        2017-04-20 12:59:18
        13.3825
        11.4890
        a05199
        2017-04-20 16:29:14
        13.4123
        11.5023
        a05200
        2017-04-20 19:59:09
        13.3301
        11.4724
        a05201
        2017-04-20 23:29:04
        13.4156
        11.5317
        a05202
        2017-04-21 02:58:59
        13.3374
        11.4784

        MS2 and Spectral Stats

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.
        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).
        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.
        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.
        Created with MultiQC

        Pipeline Spectrum Tracking

        This plot shows the tracking of the number of spectra along the quantms pipeline
        This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the `remove_decoy` parameter.: * MS1_Num: The number of MS1 spectra extracted from mzMLs * MS2_Num: The number of MS2 spectra extracted from mzMLs * MSGF: The Number of spectra identified by MSGF search engine * Comet: The Number of spectra identified by Comet search engine * Sage: The Number of spectra identified by Sage search engine * PSMs from quant. peptides: extracted from PSM table in mzTab file * Peptides quantified: extracted from PSM table in mzTab file
        Showing 11/11 rows and 6/6 columns.
        Spectra File#MS1 Spectra#MS2 SpectraMSGFComet#PSMs from quant. peptides#Peptides quantified
        a05191
        13776
        71049
        57924
        56965
        46612
        32650
        a05192
        13613
        73549
        60638
        59470
        49573
        35498
        a05194
        13450
        74198
        60932
        59956
        49928
        36652
        a05195
        13296
        72696
        59554
        58516
        48629
        35805
        a05196
        13420
        73023
        59967
        58738
        48929
        36063
        a05197
        13258
        73458
        60032
        59227
        49038
        36150
        a05198
        13423
        73438
        60113
        59008
        48700
        35939
        a05199
        13443
        73666
        60381
        59432
        49130
        35596
        a05200
        12979
        73290
        60109
        59215
        49118
        36025
        a05201
        13360
        74857
        61266
        60081
        49555
        36140
        a05202
        13243
        73524
        60284
        59242
        48636
        35837

        Distribution of Precursor Charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.
        Created with MultiQC

        Charge-state of Per File

        The distribution of precursor ion charge states (based on mzTab data).
        The distribution of precursor ion charge states (based on mzTab data).
        Created with MultiQC

        MS/MS Counts Per 3D-peak

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.
        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.
        Created with MultiQC


        Software Versions

        Software Versions lists versions of software tools extracted from file contents.

        GroupSoftwareVersion
        CONSENSUSIDConsensusID3.3.0-pre-exported-20250122
        DECOYDATABASEDecoyDatabase3.3.0-pre-exported-20250122
        FDRCONSENSUSIDFalseDiscoveryRate3.3.0-pre-exported-20250122
        IDFILTERIDFilter3.3.0-pre-exported-20250122
        MS2RESCOREMS2Rescore3.1.4
        deeplc3.1.8
        ms2pip4.1.0
        quantms-rescoring0.0.5
        MSGFDBINDEXINGmsgf_plusMS-GF+ Release (v2024.03.26) (26 March 2024)
        MZMLSTATISTICSquantms-utils0.0.20
        PERCOLATORPercolatorAdapter3.3.0-pre-exported-20250122
        percolator3.05.0, Build Date Aug 31 2020 19:03:04
        PROTEOMICSLFQProteomicsLFQ3.3.0-pre-exported-20250122
        SAMPLESHEET_CHECKquantms-utils0.0.20
        SDRFPARSINGsdrf-pipelines0.0.31
        SEARCHENGINECOMETComet2023.01 rev. 2
        CometAdapter3.3.0-pre-exported-20250122
        SEARCHENGINEMSGFMSGFPlusAdapter3.3.0-pre-exported-20250122
        msgf_plusMS-GF+ Release (v2023.01.12) (12 January 2023)
        THERMORAWFILEPARSERThermoRawFileParser1.3.4
        WorkflowNextflow24.10.5
        bigbio/quantmsv1.4.0dev

        bigbio/quantms Workflow Summary

        - this information is collected when the pipeline is started.URL: https://github.com/bigbio/quantms

        Input/output options

        export_decoy_psm
        true
        input
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv
        local_input_type
        raw
        outdir
        ./PXD007683_msgf_comet_ms2rescore_quant
        root_folder
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683

        SDRF validation

        skip_factor_validation
        true
        use_ols_cache_only
        true
        validate_ontologies
        true

        Protein database

        add_decoys
        true
        database
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta

        Database search

        search_engines
        msgf,comet

        Modification localization

        luciphor_debug
        0

        PSM re-scoring (general)

        ms2pip_model
        HCD
        ms2pip_model_dir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/
        ms2rescore
        true
        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        0

        Consensus ID

        consensusid_considered_top_hits
        0
        min_consensus_support
        0

        Isobaric analyzer

        quant_activation_method
        HCD

        Protein Quantification (LFQ)

        feature_with_id_min_score
        0.10

        Statistical post-processing

        contrasts
        pairwise
        skip_post_msstats
        true

        Quality control

        enable_pmultiqc
        true
        pmultiqc_idxml_skip
        true

        Generic options

        trace_report_suffix
        2025-03-30_11-14-03

        Core Nextflow options

        configFiles
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/nextflow.config, /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config
        containerEngine
        docker
        launchDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
        profile
        docker
        projectDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms
        runName
        mad_hodgkin
        userName
        daicx
        workDir
        /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/gitrepo/new_quantms/quantms/work

        bigbio/quantms Methods Description

        Suggested text and references to use when describing pipeline usage within the methods section of a publication.URL: https://github.com/bigbio/quantms

        Methods

        Data was processed using bigbio/quantms v1.4.0dev (doi: 10.5281/zenodo.7754148) of the nf-core collection of workflows (Ewels et al., 2020), utilising reproducible software environments from the Bioconda (Grüning et al., 2018) and Biocontainers (da Veiga Leprevost et al., 2017) projects.

        The pipeline was executed with Nextflow v24.10.5 (Di Tommaso et al., 2017) with the following command:

        nextflow run main.nf --input /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/PXD007683-LFQ.sdrf.tsv --database /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683/uniprot-UP000005640_UP000002311_reviewed.fasta --add_decoys true --search_engines msgf,comet -resume PXD007683 -c /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/k8s_next.config -profile docker --ms2rescore true --ms2pip_model_dir /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/model_file/ --root_folder /mnt/daicx/pvc-afbfaa68-aa52-416c-b273-64fb016fd745/mcp/PXD007683 --local_input_type raw --ms2pip_model HCD --outdir ./PXD007683_msgf_comet_ms2rescore_quant --skip_post_msstats true

        References

        • Di Tommaso, P., Chatzou, M., Floden, E. W., Barja, P. P., Palumbo, E., & Notredame, C. (2017). Nextflow enables reproducible computational workflows. Nature Biotechnology, 35(4), 316-319. doi: 10.1038/nbt.3820
        • Ewels, P. A., Peltzer, A., Fillinger, S., Patel, H., Alneberg, J., Wilm, A., Garcia, M. U., Di Tommaso, P., & Nahnsen, S. (2020). The nf-core framework for community-curated bioinformatics pipelines. Nature Biotechnology, 38(3), 276-278. doi: 10.1038/s41587-020-0439-x
        • Grüning, B., Dale, R., Sjödin, A., Chapman, B. A., Rowe, J., Tomkins-Tinch, C. H., Valieris, R., Köster, J., & Bioconda Team. (2018). Bioconda: sustainable and comprehensive software distribution for the life sciences. Nature Methods, 15(7), 475–476. doi: 10.1038/s41592-018-0046-7
        • da Veiga Leprevost, F., Grüning, B. A., Alves Aflitos, S., Röst, H. L., Uszkoreit, J., Barsnes, H., Vaudel, M., Moreno, P., Gatto, L., Weber, J., Bai, M., Jimenez, R. C., Sachsenberg, T., Pfeuffer, J., Vera Alvarez, R., Griss, J., Nesvizhskii, A. I., & Perez-Riverol, Y. (2017). BioContainers: an open-source and community-driven framework for software standardization. Bioinformatics (Oxford, England), 33(16), 2580–2582. doi: 10.1093/bioinformatics/btx192
        Notes:
        • The command above does not include parameters contained in any configs or profiles that may have been used. Ensure the config file is also uploaded with your publication!
        • You should also cite all software used within this run. Check the "Software Versions" of this report to get version information.